Hi, I think you add lots of data augmentation to the validation set. This results in a large gap between the training and validation sets. This can lead to a situation where a model performs well, possibly simply due to a closer augmentation setup used in the validation set. It is very unfair when compared methods on the validation set. So, I suggest you provide detailed data augmentation setup or codes mentioned in the paper.
Hi, I think you add lots of data augmentation to the validation set. This results in a large gap between the training and validation sets. This can lead to a situation where a model performs well, possibly simply due to a closer augmentation setup used in the validation set. It is very unfair when compared methods on the validation set. So, I suggest you provide detailed data augmentation setup or codes mentioned in the paper.